Distributed Estimation with Partially Overlapping States based on Deterministic Sample-based Fusion
Susanne Radtke, Benjamin Noack, Uwe D. Hanebeck · 2019
Distributing workload between sensor nodes is a practical solution to monitor large-scale phenomena. In doing so, the system can be split into smaller subsystems that can be estimated and controlled more easily. While current state-of-the-art fusion methods for distributed estimation assume the fusion of estimates referring to the full dimension of the state, little effort has been made to account for the fusion of unequal state vectors referring to smaller subsystems of the full system. In this paper, a novel method to fuse overlapping state vectors using a deterministic sample-based fusion method is proposed. These deterministic samples can be used to account for the correlated and uncorrelated noise terms and are therefore able to reconstruct the joint covariance matrix in a distributed fashion. The performance of the proposed fusion method is compared to other state-of-the-art methods.